Abstract: A content management system receives a plurality of topics extracted from documents stored by the system. Pairings between the topics are generated, where a pairing between two topics is generated responsive to the two topics appearing in proximity to one another in one or more of the documents. A complexity of each received topic is also determined. The content management system generates a progression of the topics based on the complexity of the topics and the pairings between the topics. The progression comprises a sequential ordering of paired topics, in which a topic in the ordering has a higher complexity than a preceding topic. Responsive to a user of the content management system accessing content associated with a topic in the progression, a next topic in the progression is recommended to the user.

Abstract: A content management system receives a plurality of topics extracted from documents stored by the system. Pairings between the topics are generated, where a pairing between two topics is generated responsive to the two topics appearing in proximity to one another in one or more of the documents. A complexity of each received topic is also determined. The content management system generates a progression of the topics based on the complexity of the topics and the pairings between the topics. The progression comprises a sequential ordering of paired topics, in which a topic in the ordering has a higher complexity than a preceding topic. Responsive to a user of the content management system accessing content associated with a topic in the progression, a next topic in the progression is recommended to the user.

Abstract: An online system generates web pages for user-generated questions that are structured to rank highly in search results generated by external search engines. The online system receives a question uploaded to the online system by a user. The question includes media content, such as an image, a voice recording, or a video. The online system transcribes the media content of the question and applies a web page template to the question content to generate a web page. The template includes a metadata description, a breadcrumb, and a uniform resource locator. At least one of the metadata description, breadcrumb, and uniform resource locator comprises a portion of the transcribed media content of the question. The online system publishes the web page at a location specified by the uniform resource locator.

Abstract: Documents of a content management system are classified into a hierarchical taxonomy comprising a hierarchy of nodes, such that each document is associated with a node in the hierarchical taxonomy. For each of a plurality of topics extracted from the documents, a topic extraction system determines an affinity of the topic to respective nodes of the hierarchical taxonomy. Based on the determined affinities, a topic graph is generated for display to a user. The topic graph identifies one or more nodes of the hierarchical taxonomy and a plurality of topics associated with each of the one or more nodes, and each topic is linked to a corresponding node in the topic graph. Responsive to receiving a selection of a topic in the topic graph, identifiers of documents from which the selected topic was extracted are displayed.

Abstract: A content classification system classifies documents of a plurality of content entities into a hierarchical discipline structure. The content classification system receives a set of taxonomic labels collectively defining a hierarchical taxonomy and a plurality of documents. Each document is associated with one of the content entities. The content classification system extracts features from the received documents. A learned model is generated for assigning taxonomic labels to documents associated with a representative content entity using the features extracted from documents associated with the representative content entity. The content classification system assigns one or more taxonomic labels to each document of the other content entities using the learned model applied to the features extracted from the respective document. The documents of the plurality of content entities are classified based on the assigned taxonomic labels.